Weiyi Sun1, Anna Rumshisky2, Ozlem Uzuner3. 1. Department of Informatics, University at Albany, SUNY. Albany, NY wsun2@albany.edu. 2. Department of Computer Science, University of Massachusetts Lowell. Lowell, MA. 3. Department of Information Studies, University at Albany, SUNY. Albany, NY.
Abstract
OBJECTIVE: To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives. METHODS: We analyzed the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothesis. We annotated the RI-TIMEXes in three corpora to study the characteristics of RI-TMEXes in different domains. This informed the design of our RI-TIMEX normalization system for the clinical domain, which consists of an anchor point classifier, an anchor relation classifier, and a rule-based RI-TIMEX text span parser. We experimented with different feature sets and performed an error analysis for each system component. RESULTS: The annotation confirmed the hypotheses that we can simplify the RI-TIMEXes normalization task using two multi-label classifiers. Our system achieves anchor point classification, anchor relation classification, and rule-based parsing accuracy of 74.68%, 87.71%, and 57.2% (82.09% under relaxed matching criteria), respectively, on the held-out test set of the 2012 i2b2 temporal relation challenge. DISCUSSION: Experiments with feature sets reveal some interesting findings, such as: the verbal tense feature does not inform the anchor relation classification in clinical narratives as much as the tokens near the RI-TIMEX. Error analysis showed that underrepresented anchor point and anchor relation classes are difficult to detect. CONCLUSIONS: We formulate the RI-TIMEX normalization problem as a pair of multi-label classification problems. Considering only RI-TIMEX extraction and normalization, the system achieves statistically significant improvement over the RI-TIMEX results of the best systems in the 2012 i2b2 challenge.
OBJECTIVE: To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives. METHODS: We analyzed the RI-TIMEXes in temporally annotated corpora and propose two hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative domain: the anchor point hypothesis and the anchor relation hypothesis. We annotated the RI-TIMEXes in three corpora to study the characteristics of RI-TMEXes in different domains. This informed the design of our RI-TIMEX normalization system for the clinical domain, which consists of an anchor point classifier, an anchor relation classifier, and a rule-based RI-TIMEX text span parser. We experimented with different feature sets and performed an error analysis for each system component. RESULTS: The annotation confirmed the hypotheses that we can simplify the RI-TIMEXes normalization task using two multi-label classifiers. Our system achieves anchor point classification, anchor relation classification, and rule-based parsing accuracy of 74.68%, 87.71%, and 57.2% (82.09% under relaxed matching criteria), respectively, on the held-out test set of the 2012 i2b2 temporal relation challenge. DISCUSSION: Experiments with feature sets reveal some interesting findings, such as: the verbal tense feature does not inform the anchor relation classification in clinical narratives as much as the tokens near the RI-TIMEX. Error analysis showed that underrepresented anchor point and anchor relation classes are difficult to detect. CONCLUSIONS: We formulate the RI-TIMEX normalization problem as a pair of multi-label classification problems. Considering only RI-TIMEX extraction and normalization, the system achieves statistically significant improvement over the RI-TIMEX results of the best systems in the 2012 i2b2 challenge.
Authors: Sunghwan Sohn; Kavishwar B Wagholikar; Dingcheng Li; Siddhartha R Jonnalagadda; Cui Tao; Ravikumar Komandur Elayavilli; Hongfang Liu Journal: J Am Med Inform Assoc Date: 2013-04-04 Impact factor: 4.497
Authors: Aleksandar Kovacevic; Azad Dehghan; Michele Filannino; John A Keane; Goran Nenadic Journal: J Am Med Inform Assoc Date: 2013-04-20 Impact factor: 4.497